Coalition-Committees as Network Interventions: Baseline Network Composition in Context of Childhood Obesity Prevention Interventions

Community coalitions can address local issues with deep, historic, and contextual understanding that enables customized implementation of evidence-based strategies. The individuals within the coalition, their partnerships, and the social context is likely an important component of unraveling the challenges of implementation so interventions reach people in need. We focus on the relevance of baseline coalition-committee network (CCN), the networks of purposely formed subcommittees within community coalitions, structure as one of the moderating, theoretical links between community coalition social networks and intervention success. We explore the baseline composition and characteristics of five CCNs at the beginning of childhood obesity prevention interventions. Using a combination of social network, multidimensional scaling, and correspondence analyses, we examine the structure and heterogeneity of five CCNs, each consisting of a core group of stakeholders in the coalition and sometimes the broader community itself. Cross-sectional analyses are used to examine the composition of coalition-committees related to network density, centralization, hierarchy, and coalition demographics and characteristics. Results indicate that CCNs are patterned in their structure and characteristics, and we discuss whether adjustments to childhood obesity prevention interventions according to baseline structure and characteristics could be advantageous for intervention implementation. Together, these findings can inform future longitudinal investigations into CCN network structure

Nestedness in Networks: A Theoretical Model and Some Applications

We develop a dynamic network formation model that can explain the observed nestedness in real-world networks. Links are formed on the basis of agents’ centrality and have an exponentially distributed life time. We use stochastic stability to identify the networks to which the network formation process converges and find that they are nested split graphs. We completely determine the topological properties of the stochastically stable networks and show that they match features exhibited by real-world networks. Using four different network datasets, we empirically test our model and show that it fits well the observed networks.Nestedness, Bonacich centrality, network formation, nested split graphs

A Complex Social Network Analyses of Online Finanical Communties in Times of Geopolitcal Military and Terrorist Events

Given the advances in technology the field of social network analysis has very much hit the forefront in recent years. The information age harnesses the use of social network analysis for multiple industries and for solving complex problems. Social network analysis is an important tool in the world of the military and counter intelligence, whether it’s the capture of Osama Bin Laden or uncovering hidden Al Qaeda terrorist networks, the world around us is built on networks, be that hidden or otherwise. Online social networks give new information in the world of intelligence agencies similarly online financial communities such as Yahoo Finance gives intelligent information to knowledge hungry investors. This thesis is concerned with the exploration and exploitation of online financial community dynamics and networks using social network analysis (SNA) as a mechanism. Social network analysis measurement techniques will be applied to understand the reaction of online investors to military and terrorist geopolitical events, the stock market’s reaction to these events and if it is possible to predict military stock prices after military and terrorist geopolitical events

A network centrality bias:Central individuals in workplace networks have more supportive coworkers

Combining the results of two empirical studies, we investigate the role of alters’ motivation in explaining change in ego's network position over time. People high in communal motives, who are prone to supportive and altruistic behavior in their interactions with others as a way to gain social acceptance, prefer to establish ties with co-workers occupying central positions in organizational social networks. This effect results in a systematic network centrality bias: The personal network of central individuals (individuals with many incoming ties from colleagues) is more likely to contain more supportive and altruistic people than the personal network of individuals who are less central (individuals with fewer incoming ties). This result opens the door to the possibility that the effects of centrality so frequently documented in empirical studies may be due, at least in part, to characteristics of the alters in an ego's personal community, rather than to egos themselves. Our findings invite further empirical research on how alters’ motives affect the returns that people can reap from their personal networks in organizations.</p

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum

Using network analysis to study globalization, regionalization, and multi-polarity—Introduction to special section

n this introduction to the special section on globalization, regionalization, and multi-polarity, we review network analysis applications to the study of globalization as a complex and multi-dimensional phenomenon and we explore the frontiers of our knowledge about the network properties of global systems. We focus on the global economic (trade and investment), political, and migration systems

Character Constellations

Fiction has a major social impact, not least because it co-shapes the image that society has of various social groups. Drawing on a collection of 170 contemporary Dutch-language novels, Character Constellations presents a range of data-driven, statistical models to study depictions of characters in terms of gender, race, ethnicity, class, age, sexuality, and other identity categories. Incorporating the tools of network analysis, each chapter highlights an aspect of fictional social networks that affects the representation of social groups: their centrality, their communities, and their conflicts. While reading individual novels in light of emerging statistical patterns, combining the formal methods of social network analysis with the interpretive tools of narratology, this study shows how central societal themes such as (in)equality and emancipation, integration and segregation, and social mobility and class struggle are foregrounded, replicated, or distorted in the Dutch novel. Showcasing what character-based critiques of literary representation gain by integrating data-driven methods into the practice of critical close reading, Character Constellations contributes to societal debates on cultural representation and identity and the role fiction and art have in those debates

“Envisioning Digital Sanctuaries”: An Exploration of Virtual Collectives for Nurturing Professional Development of Women in Technical Domains

Work and learning are essential facets of our existence, yet sociocultural barriers have historically limited access and opportunity for women in multiple contexts, including their professional pursuits. Such sociocultural barriers are particularly pronounced in technical domains and have relegated minoritized voices to the margins. As a result of these barriers, those affected have suffered strife, turmoil, and subjugation. Hence, it is important to investigate how women can subvert such structural limitations and find channels through which they can seek support and guidance to navigate their careers. With the proliferation of modern communication infrastructure, virtual forums of conversation such as Reddit have emerged as key spaces that allow knowledge-sharing, provide opportunities for mobilizing collective action, and constitute sanctuaries of support and companionship. Yet, recent scholarship points to the negative ramifications of such channels in perpetuating social prejudice, directed particularly at members from historically underrepresented communities. Using a novel comparative muti-method, multi-level empirical approach comprising content analysis, social network analysis, and psycholinguistic analysis, I explore the way in which virtual forums engender community and foster avenues for everyday resilience and collective care through the analysis of 400,267 conversational traces collected from three subreddits (r/cscareerquestions, r/girlsgonewired & r/careerwoman). Blending the empirical analysis with a novel theoretical apparatus that integrates insights from social constructivist frameworks, feminist data studies, computer-supported collaborative work, and computer-mediated communication, I highlight how gender, care, and community building intertwine and collectively impact the emergent conversational habits of these online enclaves. Key results indicate six content themes ranging from discussions on knowledge advancement to scintillating ethical probes regarding disparities manifesting in the technical workplace. Further, psycholinguistic and network insights reveal four pivotal roles that support and enrich the communities in different ways. Taken together, these insights help to postulate an emergent spectrum of relationality ranging from a more agentic to a more communal pattern of affinity building. Network insights also yield valuable inferences regarding the role of automated agents in community dynamics across the forums. A discussion is presented regarding the emergent routines of care, collective empowerment, empathy-building tactics, community sustenance initiatives, and ethical perspectives in relation to the involvement of automated agents. This dissertation contributes to the theory and practice of how virtual collectives can be designed and sustained to offer spaces for enrichment, empowerment, and advocacy, focusing on the professional development of historically underrepresented voices such as women

Understanding cluster dynamics in evolutionary economic geography : essays on the structure of networks and clusters life style

L’objectif principal de cette thèse est d’étudier l’évolution des clusters. La littérature concernant les clusters s’est longuement intéressée aux raisons de leur existence ainsi qu’à la manière dont ils favorisent l’innovation, la productivité et la croissance. Nous étudions comment ces effets durent dans le temps, poursuivant l’objectif d’identifier les déterminants de la performance dynamique des clusters. Il s’agit, ainsi, d’expliquer pourquoi certains clusters déclinent tandis que d’autres continuent à fonctionner grâce à un renouveau constant. Cette thèse adopte une approche des clusters par les réseaux. Nous défendons l’idée que les structures de réseau hétérogènes des clusters démontrent des capacités différentes à s’associer ou à se dissocier des cycles industriels/technologiques au bon moment. Ainsi, nous identifions les propriétés de structure du réseau qui favorisent la performance dynamique des clusters ou la résilience des clusters. Nous appuyons nos développements théoriques sur des regards empiriques dans deux contextes bien différents. D’une part, nous étudions les structures des clusters de l’industrie de la téléphonie mobile en Europe. D’autre part, nous analysons la structure des relations entre les producteurs de fromage d’Aculco (Mexique). Le résultat principal de ce travail montre que la hiérarchie et la disassortativité des réseaux, ainsi que les interactions entre des réseaux de natures différentes (multiplexité), influencent la capacité des clusters à éviter les lock-in négatifs, conduisant à leur déclin, et favorisent le lock-out pour la survie du cluster, c’est-à-dire la prolongation de leur vie.The main objective of this thesis is to study clusters’ evolution. The literature on clusters has widely studied why clusters exist and how they favor innovation, productivity and growth. Our concern is to study how these effects hold over time. Therefore, we aim at identifying the determinants of dynamic performance of clusters to explain why some clusters decline while others keep working by continuous renewal. To do so, this thesis approaches clusters from a network perspective. We contend that clusters with heterogeneous network structures exhibit different capacities to associate and dissociate cluster’s evolution and industrial/technological cycle at the right moment. Thus, we identify the properties of network structures that favor dynamic performance of clusters or cluster resilience. We support our theoretical developments with empirical insights in two different contexts. On the one hand, we study the structure of clusters in the European mobile phone industry. On the other hand, we analyze the structure of relations between cheese producers in Aculco (Mexico). The main result of this work is that network hierarchy, network disassortativity and the interplay between different networks (multiplexity) influence the capacity of clusters to avoid negative lock-in leading to cluster failure, and favor lock-out to enhance cluster continuation, i.e. extending the life of the cluster